Antonin Raffin and Ashley Hill

TalkRL: The Reinforcement Learning Podcast

Antonin Raffin is a researcher at the German Aerospace Center (DLR) in Munich, working in the Institute of Robotics and Mechatronics. His research is on using machine learning for controlling real robots (because simulation is not enough), with a particular interest for reinforcement learning.

Ashley Hill is doing his thesis on improving control algorithms using machine learning for real time gain tuning.

He works mainly with neuroevolution, genetic algorithms, and of course reinforcement learning, applied to mobile robots. He holds a masters degree in Machine learning, and a bachelors in Computer science from the Université Paris-Saclay.

Featured References

stable-baselines on github
Ashley Hill, Antonin Raffin primary authors.

S-RL Toolbox
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Additional References

• Learning to Drive Smoothly in Minutes, Antonin Raffin
• Multimodal SRL (best paper at ICRA): Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks, Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg
• Benchmarking Model-Based Reinforcement Learning, Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
• TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
  Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser
• Stable Baselines roadmap
• OpenAI baselines stable-baselines github pull request